106 citations · 221 across the 33 of their papers we have counts for
35 papers
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning
Haodong Chen, Shengtao Wen, Dong Liang +2
Current evaluations of multimodal unlearning typically focus on whether a model stops outputting target information. This surface-level criterion, however, masks two critical failu…
Mind the Execution Gap: Action-Semantic Mismatch in World-Model Control
Shengtao Wen, Xiang Chen, Yu Tian +3
World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronously due to communication delay,…
Toward Effective and Reliable LLM Agents via Dynamic Ontology
Xiaohui Zhang, Zequn Sun, Chengyuan Yang +3
Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks, this leaves important semant…
When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction
Feiyang Ren, Shengtao Wen, Lingbing Guo +6
Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput. Output length prediction enable…
VARM-Bench: Benchmarking Verifiable Structured Reasoning in Chinese Abusive Speech Moderation
Mingyu Yuan, Shengtao Wen, Lingbing Guo +2
The widespread circulation of abusive online content has increased the need for reliable moderation of Chinese social-media text. Existing Chinese benchmarks support label classifi…
Symbolic and Abstractive Reasoning with Complex Visual Queries
Yichi Zhang, Jingdian Lu, Zhuo Chen +4
Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data…
Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens
Yichi Zhang, Zhuo Chen, Lingbing Guo +2
Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works…
Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images
Yichi Zhang, Zhuo Chen, Lingbing Guo +2
Understanding and reasoning with abstractive information from the visual modality presents significant challenges for current multi-modal large language models (MLLMs). Among the v…
ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models
Wenbin Guo, Xin Wang, Jiaoyan Chen +3
Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond tra…
Abstractive Visual Understanding of Multi-modal Structured Knowledge: A New Perspective for MLLM Evaluation
Yichi Zhang, Zhuo Chen, Lingbing Guo +4
Multi-modal large language models (MLLMs) incorporate heterogeneous modalities into LLMs, enabling a comprehensive understanding of diverse scenarios and objects. Despite the proli…
K-ON: Stacking Knowledge On the Head Layer of Large Language Model
Lingbing Guo, Yichi Zhang, Zhongpu Bo +5
Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next tok…
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Yichi Zhang, Zhuo Chen, Lingbing Guo +8
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud h…
MKGL: Mastery of a Three-Word Language
Lingbing Guo, Zhongpu Bo, Zhuo Chen +10
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs),…
NativE: Multi-modal Knowledge Graph Completion in the Wild
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling…
Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
Yichi Zhang, Binbin Hu, Zhuo Chen +6
Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train ne…
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks withi…
Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples…
Noise-powered Multi-modal Knowledge Graph Representation Framework
Zhuo Chen, Yin Fang, Yichi Zhang +5
The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for…
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Zhuo Chen, Yichi Zhang, Yin Fang +12
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…
ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
Yangyifei Luo, Zhuo Chen, Lingbing Guo +4
Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-…
Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
Bolin Zhu, Xiaoze Liu, Xin Mao +4
The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG. The majority of E…
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
Zhuo Chen, Lingbing Guo, Yin Fang +6
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting assoc…
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
Lingbing Guo, Zhuo Chen, Jiaoyan Chen +3
Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study emb…
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Lingbing Guo, Weiqing Wang, Zhuo Chen +5
Reasoning system dynamics is one of the most important analytical approaches for many scientific studies. With the initial state of a system as input, the recent graph neural netwo…